Transport journals at a glance: traits, fit, and a quick reading map (with tiers sketch)

This guide is based on what journals say on their own homepages and author guidelines, plus personal reading notes. Scopes and policies change—always re‑check the journal website before submitting.

How to use this map

• Decide which audience you’re writing for: policy/behavior, methods/networks, systems/ITS, environment/logistics, safety/human factors, or reviews.
• Read the matching section below for what they like and what to avoid.
• Set up alerts to keep track of new work.

1) Quick map by topic/audience (not a ranking)

• Policy & behavior: Transportation Research Part A (TR‑A), Transport Policy, Journal of Transport Geography (JTG).
  Highlights: policy evaluation, travel behavior, demand modeling, spatial patterns.
  Good fit: a clear question with interpretable policy implications; credible causal/empirical design.
  Pitfalls: method for method’s sake; thin discussion.
• Methods & networks: Transportation Research Part B (TR‑B), Networks and Spatial Economics (NSE).
  Highlights: network equilibrium (UE/SO), algorithms and optimization, OD estimation, reliability.
  Good fit: a crisp methodological contribution plus evidence (sim/real).
  Pitfalls: toy data only; missing comparisons to strong baselines.
• ITS / systems & ML: Transportation Research Part C (TR‑C), IEEE Transactions on Intelligent Transportation Systems (T‑ITS).
  Highlights: sensing, spatiotemporal forecasting, perception/fusion, online control.
  Good fit: reproducible engineering with real data and system constraints.
  Pitfalls: accuracy-only reporting; ignoring latency/cost constraints.
• Environment & logistics: TR‑D (environment), TR‑E (logistics & operations).
  Highlights: emissions, mobility impacts, freight networks, operations and resilience.
  Good fit: clean metric definitions (emissions/costs) and scenario boundaries.
  Pitfalls: casual scenarios; unclear externalities.
• Safety & human factors: TR‑F, Accident Analysis & Prevention (AAP).
  Highlights: driver behavior, crash causation, human–machine interaction.
  Pitfalls: correlation instead of causation; underpowered experiments.
• Reviews: Transport Reviews.
  Highlights: field maps and systematic reviews with reproducible search strategies.
  Pitfalls: lists without synthesis; missing “what’s next”.

Mnemonic: A policy/behavior, B methodology, C systems/tech, D/E impacts/industry, F people; Transport Reviews sits on top.

2) “Write like the journal” notes

TR‑A (Policy and Practice)

• What they look for: clear problem motivation and policy meaning; causal/quasi‑experimental designs; elasticities and behavioral models.
• Common data: surveys, OD inference, panels, interventions/events.
• Avoid: novelty claims without policy impact; weak limitations section.

TR‑B (Methodological)

• What they look for: well‑posed methodological advances with proofs/algorithms, and convincing empirical evidence. State relative novelty.
• Common content: UE/SO, reliability, robust/stochastic formulations, decomposition/duality, convergence rates.
• Avoid: cherry‑picked baselines; no complexity/runtime analysis.

TR‑C & IEEE T‑ITS (Systems/ITS/ML)

• What they look for: technical correctness + appropriate use; real‑world data; discussion of system budget (latency, memory, robustness).
• Avoid: reporting only accuracy; missing ablations; no deployment angle.

TR‑D / TR‑E (Environment / Logistics)

• What they look for: consistent accounting (emissions, costs, resilience); decision‑relevant insights.
• Avoid: vague scenarios; hidden assumptions or mixed units.

TR‑F / AAP (Safety & Human Factors)

• What they look for: ethical data collection, adequate statistical power, interpretable human factors variables.
• Avoid: “significant but meaningless” effects; weak experimental design.

Transport Reviews

• What they look for: reproducible search protocol, coherent taxonomy, a roadmap of open problems.
• Avoid: grab‑bag summaries with no synthesis.

3) Writing self‑check (works for any venue)

• Positioning paragraph (150–200 words): who cares, in which setting, and what changes after your paper?
• Relative contribution: versus which baseline or strand? Show quantitative differences.
• Reproducibility: code/data/parameters/seeds; big tables ready to paste.
• Boundaries & limits: assumptions, biases, and external validity spelled out.

4) Tracking & filtering without stress

• Google Scholar alerts (topics + 2–3 authors). Prune aggressively.
• Conferences: TRB, IEEE ITSC; follow special issues back to journals.
• Keep 3–4 high‑fit journals as your main lane; scan others as background.

5) FAQ

• Do rankings matter? They do—but fit matters more for being read and cited by the right audience.
• Multiple submissions? No. But you can prep two “positioning variants” (method‑leaning / policy‑leaning).
• No open data? Provide an anonymized or synthetic pathway and full scripts.

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